{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to classify satellite images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:07.149867Z",
     "start_time": "2020-06-21T21:51:07.147860Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:08.739071Z",
     "start_time": "2020-06-21T21:51:07.151429Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.densenet import DenseNet201\n",
    "from tensorflow.keras.layers import Dense, BatchNormalization\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.regularizers import l1_l2\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:08.763283Z",
     "start_time": "2020-06-21T21:51:08.740259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:08.780393Z",
     "start_time": "2020-06-21T21:51:08.764439Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:08.789711Z",
     "start_time": "2020-06-21T21:51:08.781539Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path ('results', 'eurosat')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load EuroSat Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:09.011058Z",
     "start_time": "2020-06-21T21:51:08.791300Z"
    }
   },
   "outputs": [],
   "source": [
    "(raw_train, raw_validation), metadata = tfds.load('eurosat',\n",
    "                                                  split=[\n",
    "                                                      tfds.Split.TRAIN.subsplit(\n",
    "                                                          tfds.percent[:90]),\n",
    "                                                      tfds.Split.TRAIN.subsplit(\n",
    "                                                          tfds.percent[90:])\n",
    "                                                  ],\n",
    "                                                  with_info=True,\n",
    "                                                  shuffle_files=False,\n",
    "                                                  as_supervised=True,\n",
    "                                                  data_dir='../data/tensorflow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect MetaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:09.017292Z",
     "start_time": "2020-06-21T21:51:09.012747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tfds.core.DatasetInfo(\n",
       "    name='eurosat',\n",
       "    version=0.0.1,\n",
       "    description='EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral\n",
       "bands and consisting of 10 classes with 27000 labeled and\n",
       "geo-referenced samples.\n",
       "\n",
       "Two datasets are offered:\n",
       "- rgb: Contains only the optical R, G, B frequency bands encoded as JPEG image.\n",
       "- all: Contains all 13 bands in the original value range (float32).\n",
       "\n",
       "URL: https://github.com/phelber/eurosat\n",
       "',\n",
       "    urls=['https://github.com/phelber/eurosat'],\n",
       "    features=FeaturesDict({\n",
       "        'filename': Text(shape=(), dtype=tf.string),\n",
       "        'image': Image(shape=(64, 64, 3), dtype=tf.uint8),\n",
       "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
       "    }),\n",
       "    total_num_examples=27000,\n",
       "    splits={\n",
       "        'train': 27000,\n",
       "    },\n",
       "    supervised_keys=('image', 'label'),\n",
       "    citation=\"\"\"@misc{helber2017eurosat,\n",
       "        title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},\n",
       "        author={Patrick Helber and Benjamin Bischke and Andreas Dengel and Damian Borth},\n",
       "        year={2017},\n",
       "        eprint={1709.00029},\n",
       "        archivePrefix={arXiv},\n",
       "        primaryClass={cs.CV}\n",
       "    }\"\"\",\n",
       "    redistribution_info=,\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:09.028232Z",
     "start_time": "2020-06-21T21:51:09.018768Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:\t <_OptionsDataset shapes: ((64, 64, 3), ()), types: (tf.uint8, tf.int64)>\n",
      "Valid:\t <_OptionsDataset shapes: ((64, 64, 3), ()), types: (tf.uint8, tf.int64)>\n"
     ]
    }
   ],
   "source": [
    "print('Train:\\t', raw_train)\n",
    "print('Valid:\\t', raw_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.473848Z",
     "start_time": "2020-06-21T21:51:09.029383Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(15, 7), ncols=5, nrows=2)\n",
    "axes = axes.flatten()\n",
    "get_label_name = metadata.features['label'].int2str\n",
    "labels = set()\n",
    "c = 0\n",
    "for img, label in raw_train.as_numpy_iterator():\n",
    "    if label not in labels:\n",
    "        axes[c].imshow(img)\n",
    "        axes[c].set_title(get_label_name(label))\n",
    "        axes[c].grid(False)\n",
    "        axes[c].tick_params(axis='both', \n",
    "                            which='both', \n",
    "                            bottom=False, \n",
    "                            top=False, \n",
    "                            labelbottom=False, \n",
    "                            right=False, \n",
    "                            left=False, \n",
    "                            labelleft=False)\n",
    "        labels.add(label)\n",
    "        c += 1\n",
    "        if c == 10:\n",
    "            break\n",
    "fig.suptitle('EuroSat Satellite Images', fontsize=16)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.92)\n",
    "fig.savefig(results_path / 'eurosat_samples', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All images will be resized to 160x160:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.476448Z",
     "start_time": "2020-06-21T21:51:11.474729Z"
    }
   },
   "outputs": [],
   "source": [
    "IMG_SIZE = 64\n",
    "IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.499274Z",
     "start_time": "2020-06-21T21:51:11.477611Z"
    }
   },
   "outputs": [],
   "source": [
    "def format_example(image, label):\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = (image/127.5) - 1\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.554643Z",
     "start_time": "2020-06-21T21:51:11.500347Z"
    }
   },
   "outputs": [],
   "source": [
    "train = raw_train.map(format_example)\n",
    "validation = raw_validation.map(format_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.557775Z",
     "start_time": "2020-06-21T21:51:11.555656Z"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "SHUFFLE_BUFFER_SIZE = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.567025Z",
     "start_time": "2020-06-21T21:51:11.558910Z"
    }
   },
   "outputs": [],
   "source": [
    "train_batches = train.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
    "validation_batches = validation.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:11.731566Z",
     "start_time": "2020-06-21T21:51:11.568122Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 64, 64, 3])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for image_batch, label_batch in train_batches.take(1):\n",
    "    pass\n",
    "\n",
    "image_batch.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the DenseNet201 Bottleneck Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:19.406228Z",
     "start_time": "2020-06-21T21:51:11.732673Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"densenet201\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 64, 64, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d (ZeroPadding2D)  (None, 70, 70, 3)    0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1/conv (Conv2D)             (None, 32, 32, 64)   9408        zero_padding2d[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1/bn (BatchNormalization)   (None, 32, 32, 64)   256         conv1/conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1/relu (Activation)         (None, 32, 32, 64)   0           conv1/bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, 34, 34, 64)   0           conv1/relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 16, 16, 64)   0           zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 16, 16, 64)   256         pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_relu (Activation (None, 16, 16, 64)   0           conv2_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 16, 16, 128)  8192        conv2_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 16, 16, 128)  0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 16, 16, 32)   36864       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_concat (Concatenat (None, 16, 16, 96)   0           pool1[0][0]                      \n",
      "                                                                 conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_bn (BatchNormali (None, 16, 16, 96)   384         conv2_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_0_relu (Activation (None, 16, 16, 96)   0           conv2_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 16, 16, 128)  12288       conv2_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 16, 16, 128)  0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 16, 16, 32)   36864       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_concat (Concatenat (None, 16, 16, 128)  0           conv2_block1_concat[0][0]        \n",
      "                                                                 conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_0_relu (Activation (None, 16, 16, 128)  0           conv2_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 16, 16, 128)  16384       conv2_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 16, 16, 128)  0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 16, 16, 32)   36864       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_concat (Concatenat (None, 16, 16, 160)  0           conv2_block2_concat[0][0]        \n",
      "                                                                 conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_bn (BatchNormali (None, 16, 16, 160)  640         conv2_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_0_relu (Activation (None, 16, 16, 160)  0           conv2_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_conv (Conv2D)    (None, 16, 16, 128)  20480       conv2_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_1_relu (Activation (None, 16, 16, 128)  0           conv2_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_2_conv (Conv2D)    (None, 16, 16, 32)   36864       conv2_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block4_concat (Concatenat (None, 16, 16, 192)  0           conv2_block3_concat[0][0]        \n",
      "                                                                 conv2_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_bn (BatchNormali (None, 16, 16, 192)  768         conv2_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_0_relu (Activation (None, 16, 16, 192)  0           conv2_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_conv (Conv2D)    (None, 16, 16, 128)  24576       conv2_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_1_relu (Activation (None, 16, 16, 128)  0           conv2_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_2_conv (Conv2D)    (None, 16, 16, 32)   36864       conv2_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block5_concat (Concatenat (None, 16, 16, 224)  0           conv2_block4_concat[0][0]        \n",
      "                                                                 conv2_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_bn (BatchNormali (None, 16, 16, 224)  896         conv2_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_0_relu (Activation (None, 16, 16, 224)  0           conv2_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_conv (Conv2D)    (None, 16, 16, 128)  28672       conv2_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_bn (BatchNormali (None, 16, 16, 128)  512         conv2_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_1_relu (Activation (None, 16, 16, 128)  0           conv2_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_2_conv (Conv2D)    (None, 16, 16, 32)   36864       conv2_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block6_concat (Concatenat (None, 16, 16, 256)  0           conv2_block5_concat[0][0]        \n",
      "                                                                 conv2_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_bn (BatchNormalization)   (None, 16, 16, 256)  1024        conv2_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "pool2_relu (Activation)         (None, 16, 16, 256)  0           pool2_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool2_conv (Conv2D)             (None, 16, 16, 128)  32768       pool2_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool2_pool (AveragePooling2D)   (None, 8, 8, 128)    0           pool2_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 8, 8, 128)    512         pool2_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_relu (Activation (None, 8, 8, 128)    0           conv3_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 8, 8, 128)    16384       conv3_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 8, 8, 128)    0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_concat (Concatenat (None, 8, 8, 160)    0           pool2_pool[0][0]                 \n",
      "                                                                 conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_bn (BatchNormali (None, 8, 8, 160)    640         conv3_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_0_relu (Activation (None, 8, 8, 160)    0           conv3_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 8, 8, 128)    20480       conv3_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 8, 8, 128)    0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_concat (Concatenat (None, 8, 8, 192)    0           conv3_block1_concat[0][0]        \n",
      "                                                                 conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_bn (BatchNormali (None, 8, 8, 192)    768         conv3_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_0_relu (Activation (None, 8, 8, 192)    0           conv3_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 8, 8, 128)    24576       conv3_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 8, 8, 128)    0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_concat (Concatenat (None, 8, 8, 224)    0           conv3_block2_concat[0][0]        \n",
      "                                                                 conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_bn (BatchNormali (None, 8, 8, 224)    896         conv3_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_0_relu (Activation (None, 8, 8, 224)    0           conv3_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 8, 8, 128)    28672       conv3_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 8, 8, 128)    0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_concat (Concatenat (None, 8, 8, 256)    0           conv3_block3_concat[0][0]        \n",
      "                                                                 conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_bn (BatchNormali (None, 8, 8, 256)    1024        conv3_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_0_relu (Activation (None, 8, 8, 256)    0           conv3_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_conv (Conv2D)    (None, 8, 8, 128)    32768       conv3_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_relu (Activation (None, 8, 8, 128)    0           conv3_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_concat (Concatenat (None, 8, 8, 288)    0           conv3_block4_concat[0][0]        \n",
      "                                                                 conv3_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_bn (BatchNormali (None, 8, 8, 288)    1152        conv3_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_0_relu (Activation (None, 8, 8, 288)    0           conv3_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_conv (Conv2D)    (None, 8, 8, 128)    36864       conv3_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_relu (Activation (None, 8, 8, 128)    0           conv3_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_concat (Concatenat (None, 8, 8, 320)    0           conv3_block5_concat[0][0]        \n",
      "                                                                 conv3_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_bn (BatchNormali (None, 8, 8, 320)    1280        conv3_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_0_relu (Activation (None, 8, 8, 320)    0           conv3_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_conv (Conv2D)    (None, 8, 8, 128)    40960       conv3_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_relu (Activation (None, 8, 8, 128)    0           conv3_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_concat (Concatenat (None, 8, 8, 352)    0           conv3_block6_concat[0][0]        \n",
      "                                                                 conv3_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_bn (BatchNormali (None, 8, 8, 352)    1408        conv3_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_0_relu (Activation (None, 8, 8, 352)    0           conv3_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_conv (Conv2D)    (None, 8, 8, 128)    45056       conv3_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_relu (Activation (None, 8, 8, 128)    0           conv3_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_concat (Concatenat (None, 8, 8, 384)    0           conv3_block7_concat[0][0]        \n",
      "                                                                 conv3_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_bn (BatchNormali (None, 8, 8, 384)    1536        conv3_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_0_relu (Activation (None, 8, 8, 384)    0           conv3_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_conv (Conv2D)    (None, 8, 8, 128)    49152       conv3_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_bn (BatchNormali (None, 8, 8, 128)    512         conv3_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_1_relu (Activation (None, 8, 8, 128)    0           conv3_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv3_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block9_concat (Concatenat (None, 8, 8, 416)    0           conv3_block8_concat[0][0]        \n",
      "                                                                 conv3_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_bn (BatchNormal (None, 8, 8, 416)    1664        conv3_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_0_relu (Activatio (None, 8, 8, 416)    0           conv3_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_conv (Conv2D)   (None, 8, 8, 128)    53248       conv3_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_bn (BatchNormal (None, 8, 8, 128)    512         conv3_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_1_relu (Activatio (None, 8, 8, 128)    0           conv3_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_2_conv (Conv2D)   (None, 8, 8, 32)     36864       conv3_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block10_concat (Concatena (None, 8, 8, 448)    0           conv3_block9_concat[0][0]        \n",
      "                                                                 conv3_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_bn (BatchNormal (None, 8, 8, 448)    1792        conv3_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_0_relu (Activatio (None, 8, 8, 448)    0           conv3_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_conv (Conv2D)   (None, 8, 8, 128)    57344       conv3_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_bn (BatchNormal (None, 8, 8, 128)    512         conv3_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_1_relu (Activatio (None, 8, 8, 128)    0           conv3_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_2_conv (Conv2D)   (None, 8, 8, 32)     36864       conv3_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block11_concat (Concatena (None, 8, 8, 480)    0           conv3_block10_concat[0][0]       \n",
      "                                                                 conv3_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_bn (BatchNormal (None, 8, 8, 480)    1920        conv3_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_0_relu (Activatio (None, 8, 8, 480)    0           conv3_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_conv (Conv2D)   (None, 8, 8, 128)    61440       conv3_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_bn (BatchNormal (None, 8, 8, 128)    512         conv3_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_1_relu (Activatio (None, 8, 8, 128)    0           conv3_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_2_conv (Conv2D)   (None, 8, 8, 32)     36864       conv3_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block12_concat (Concatena (None, 8, 8, 512)    0           conv3_block11_concat[0][0]       \n",
      "                                                                 conv3_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_bn (BatchNormalization)   (None, 8, 8, 512)    2048        conv3_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool3_relu (Activation)         (None, 8, 8, 512)    0           pool3_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool3_conv (Conv2D)             (None, 8, 8, 256)    131072      pool3_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool3_pool (AveragePooling2D)   (None, 4, 4, 256)    0           pool3_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 4, 4, 256)    1024        pool3_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_relu (Activation (None, 4, 4, 256)    0           conv4_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 4, 4, 128)    32768       conv4_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 4, 4, 128)    0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_concat (Concatenat (None, 4, 4, 288)    0           pool3_pool[0][0]                 \n",
      "                                                                 conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_bn (BatchNormali (None, 4, 4, 288)    1152        conv4_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_0_relu (Activation (None, 4, 4, 288)    0           conv4_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 4, 4, 128)    36864       conv4_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 4, 4, 128)    0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_concat (Concatenat (None, 4, 4, 320)    0           conv4_block1_concat[0][0]        \n",
      "                                                                 conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_bn (BatchNormali (None, 4, 4, 320)    1280        conv4_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_0_relu (Activation (None, 4, 4, 320)    0           conv4_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 4, 4, 128)    40960       conv4_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 4, 4, 128)    0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_concat (Concatenat (None, 4, 4, 352)    0           conv4_block2_concat[0][0]        \n",
      "                                                                 conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_bn (BatchNormali (None, 4, 4, 352)    1408        conv4_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_0_relu (Activation (None, 4, 4, 352)    0           conv4_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 4, 4, 128)    45056       conv4_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 4, 4, 128)    0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_concat (Concatenat (None, 4, 4, 384)    0           conv4_block3_concat[0][0]        \n",
      "                                                                 conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_bn (BatchNormali (None, 4, 4, 384)    1536        conv4_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_0_relu (Activation (None, 4, 4, 384)    0           conv4_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 4, 4, 128)    49152       conv4_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 4, 4, 128)    0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_concat (Concatenat (None, 4, 4, 416)    0           conv4_block4_concat[0][0]        \n",
      "                                                                 conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_bn (BatchNormali (None, 4, 4, 416)    1664        conv4_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_0_relu (Activation (None, 4, 4, 416)    0           conv4_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 4, 4, 128)    53248       conv4_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 4, 4, 128)    0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_concat (Concatenat (None, 4, 4, 448)    0           conv4_block5_concat[0][0]        \n",
      "                                                                 conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_bn (BatchNormali (None, 4, 4, 448)    1792        conv4_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_0_relu (Activation (None, 4, 4, 448)    0           conv4_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_conv (Conv2D)    (None, 4, 4, 128)    57344       conv4_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_relu (Activation (None, 4, 4, 128)    0           conv4_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_concat (Concatenat (None, 4, 4, 480)    0           conv4_block6_concat[0][0]        \n",
      "                                                                 conv4_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_bn (BatchNormali (None, 4, 4, 480)    1920        conv4_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_0_relu (Activation (None, 4, 4, 480)    0           conv4_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_conv (Conv2D)    (None, 4, 4, 128)    61440       conv4_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_relu (Activation (None, 4, 4, 128)    0           conv4_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_concat (Concatenat (None, 4, 4, 512)    0           conv4_block7_concat[0][0]        \n",
      "                                                                 conv4_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_bn (BatchNormali (None, 4, 4, 512)    2048        conv4_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_0_relu (Activation (None, 4, 4, 512)    0           conv4_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_conv (Conv2D)    (None, 4, 4, 128)    65536       conv4_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_bn (BatchNormali (None, 4, 4, 128)    512         conv4_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_relu (Activation (None, 4, 4, 128)    0           conv4_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv4_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_concat (Concatenat (None, 4, 4, 544)    0           conv4_block8_concat[0][0]        \n",
      "                                                                 conv4_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_bn (BatchNormal (None, 4, 4, 544)    2176        conv4_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_0_relu (Activatio (None, 4, 4, 544)    0           conv4_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_conv (Conv2D)   (None, 4, 4, 128)    69632       conv4_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_concat (Concatena (None, 4, 4, 576)    0           conv4_block9_concat[0][0]        \n",
      "                                                                 conv4_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_bn (BatchNormal (None, 4, 4, 576)    2304        conv4_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_0_relu (Activatio (None, 4, 4, 576)    0           conv4_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_conv (Conv2D)   (None, 4, 4, 128)    73728       conv4_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_concat (Concatena (None, 4, 4, 608)    0           conv4_block10_concat[0][0]       \n",
      "                                                                 conv4_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_bn (BatchNormal (None, 4, 4, 608)    2432        conv4_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_0_relu (Activatio (None, 4, 4, 608)    0           conv4_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_conv (Conv2D)   (None, 4, 4, 128)    77824       conv4_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_concat (Concatena (None, 4, 4, 640)    0           conv4_block11_concat[0][0]       \n",
      "                                                                 conv4_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_bn (BatchNormal (None, 4, 4, 640)    2560        conv4_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_0_relu (Activatio (None, 4, 4, 640)    0           conv4_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_conv (Conv2D)   (None, 4, 4, 128)    81920       conv4_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_concat (Concatena (None, 4, 4, 672)    0           conv4_block12_concat[0][0]       \n",
      "                                                                 conv4_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_bn (BatchNormal (None, 4, 4, 672)    2688        conv4_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_0_relu (Activatio (None, 4, 4, 672)    0           conv4_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_conv (Conv2D)   (None, 4, 4, 128)    86016       conv4_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_concat (Concatena (None, 4, 4, 704)    0           conv4_block13_concat[0][0]       \n",
      "                                                                 conv4_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_bn (BatchNormal (None, 4, 4, 704)    2816        conv4_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_0_relu (Activatio (None, 4, 4, 704)    0           conv4_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_conv (Conv2D)   (None, 4, 4, 128)    90112       conv4_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_concat (Concatena (None, 4, 4, 736)    0           conv4_block14_concat[0][0]       \n",
      "                                                                 conv4_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_bn (BatchNormal (None, 4, 4, 736)    2944        conv4_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_0_relu (Activatio (None, 4, 4, 736)    0           conv4_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_conv (Conv2D)   (None, 4, 4, 128)    94208       conv4_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_concat (Concatena (None, 4, 4, 768)    0           conv4_block15_concat[0][0]       \n",
      "                                                                 conv4_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_bn (BatchNormal (None, 4, 4, 768)    3072        conv4_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_0_relu (Activatio (None, 4, 4, 768)    0           conv4_block17_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_conv (Conv2D)   (None, 4, 4, 128)    98304       conv4_block17_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_concat (Concatena (None, 4, 4, 800)    0           conv4_block16_concat[0][0]       \n",
      "                                                                 conv4_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_bn (BatchNormal (None, 4, 4, 800)    3200        conv4_block17_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_0_relu (Activatio (None, 4, 4, 800)    0           conv4_block18_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_conv (Conv2D)   (None, 4, 4, 128)    102400      conv4_block18_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_concat (Concatena (None, 4, 4, 832)    0           conv4_block17_concat[0][0]       \n",
      "                                                                 conv4_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_bn (BatchNormal (None, 4, 4, 832)    3328        conv4_block18_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_0_relu (Activatio (None, 4, 4, 832)    0           conv4_block19_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_conv (Conv2D)   (None, 4, 4, 128)    106496      conv4_block19_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_concat (Concatena (None, 4, 4, 864)    0           conv4_block18_concat[0][0]       \n",
      "                                                                 conv4_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_bn (BatchNormal (None, 4, 4, 864)    3456        conv4_block19_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_0_relu (Activatio (None, 4, 4, 864)    0           conv4_block20_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_conv (Conv2D)   (None, 4, 4, 128)    110592      conv4_block20_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_concat (Concatena (None, 4, 4, 896)    0           conv4_block19_concat[0][0]       \n",
      "                                                                 conv4_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_bn (BatchNormal (None, 4, 4, 896)    3584        conv4_block20_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_0_relu (Activatio (None, 4, 4, 896)    0           conv4_block21_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_conv (Conv2D)   (None, 4, 4, 128)    114688      conv4_block21_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_concat (Concatena (None, 4, 4, 928)    0           conv4_block20_concat[0][0]       \n",
      "                                                                 conv4_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_bn (BatchNormal (None, 4, 4, 928)    3712        conv4_block21_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_0_relu (Activatio (None, 4, 4, 928)    0           conv4_block22_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_conv (Conv2D)   (None, 4, 4, 128)    118784      conv4_block22_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_concat (Concatena (None, 4, 4, 960)    0           conv4_block21_concat[0][0]       \n",
      "                                                                 conv4_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_bn (BatchNormal (None, 4, 4, 960)    3840        conv4_block22_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_0_relu (Activatio (None, 4, 4, 960)    0           conv4_block23_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_conv (Conv2D)   (None, 4, 4, 128)    122880      conv4_block23_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_concat (Concatena (None, 4, 4, 992)    0           conv4_block22_concat[0][0]       \n",
      "                                                                 conv4_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_bn (BatchNormal (None, 4, 4, 992)    3968        conv4_block23_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_0_relu (Activatio (None, 4, 4, 992)    0           conv4_block24_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_conv (Conv2D)   (None, 4, 4, 128)    126976      conv4_block24_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_concat (Concatena (None, 4, 4, 1024)   0           conv4_block23_concat[0][0]       \n",
      "                                                                 conv4_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_0_bn (BatchNormal (None, 4, 4, 1024)   4096        conv4_block24_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_0_relu (Activatio (None, 4, 4, 1024)   0           conv4_block25_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_1_conv (Conv2D)   (None, 4, 4, 128)    131072      conv4_block25_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block25_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block25_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block25_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_concat (Concatena (None, 4, 4, 1056)   0           conv4_block24_concat[0][0]       \n",
      "                                                                 conv4_block25_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_0_bn (BatchNormal (None, 4, 4, 1056)   4224        conv4_block25_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_0_relu (Activatio (None, 4, 4, 1056)   0           conv4_block26_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_1_conv (Conv2D)   (None, 4, 4, 128)    135168      conv4_block26_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block26_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block26_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block26_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_concat (Concatena (None, 4, 4, 1088)   0           conv4_block25_concat[0][0]       \n",
      "                                                                 conv4_block26_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_0_bn (BatchNormal (None, 4, 4, 1088)   4352        conv4_block26_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_0_relu (Activatio (None, 4, 4, 1088)   0           conv4_block27_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_1_conv (Conv2D)   (None, 4, 4, 128)    139264      conv4_block27_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block27_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block27_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block27_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_concat (Concatena (None, 4, 4, 1120)   0           conv4_block26_concat[0][0]       \n",
      "                                                                 conv4_block27_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_0_bn (BatchNormal (None, 4, 4, 1120)   4480        conv4_block27_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_0_relu (Activatio (None, 4, 4, 1120)   0           conv4_block28_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_1_conv (Conv2D)   (None, 4, 4, 128)    143360      conv4_block28_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block28_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block28_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block28_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_concat (Concatena (None, 4, 4, 1152)   0           conv4_block27_concat[0][0]       \n",
      "                                                                 conv4_block28_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_0_bn (BatchNormal (None, 4, 4, 1152)   4608        conv4_block28_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_0_relu (Activatio (None, 4, 4, 1152)   0           conv4_block29_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_1_conv (Conv2D)   (None, 4, 4, 128)    147456      conv4_block29_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block29_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block29_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block29_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_concat (Concatena (None, 4, 4, 1184)   0           conv4_block28_concat[0][0]       \n",
      "                                                                 conv4_block29_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_0_bn (BatchNormal (None, 4, 4, 1184)   4736        conv4_block29_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_0_relu (Activatio (None, 4, 4, 1184)   0           conv4_block30_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_1_conv (Conv2D)   (None, 4, 4, 128)    151552      conv4_block30_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block30_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block30_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block30_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_concat (Concatena (None, 4, 4, 1216)   0           conv4_block29_concat[0][0]       \n",
      "                                                                 conv4_block30_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_0_bn (BatchNormal (None, 4, 4, 1216)   4864        conv4_block30_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_0_relu (Activatio (None, 4, 4, 1216)   0           conv4_block31_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_1_conv (Conv2D)   (None, 4, 4, 128)    155648      conv4_block31_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block31_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block31_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block31_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_concat (Concatena (None, 4, 4, 1248)   0           conv4_block30_concat[0][0]       \n",
      "                                                                 conv4_block31_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_0_bn (BatchNormal (None, 4, 4, 1248)   4992        conv4_block31_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_0_relu (Activatio (None, 4, 4, 1248)   0           conv4_block32_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_1_conv (Conv2D)   (None, 4, 4, 128)    159744      conv4_block32_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block32_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block32_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block32_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_concat (Concatena (None, 4, 4, 1280)   0           conv4_block31_concat[0][0]       \n",
      "                                                                 conv4_block32_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_0_bn (BatchNormal (None, 4, 4, 1280)   5120        conv4_block32_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_0_relu (Activatio (None, 4, 4, 1280)   0           conv4_block33_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_1_conv (Conv2D)   (None, 4, 4, 128)    163840      conv4_block33_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block33_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block33_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block33_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_concat (Concatena (None, 4, 4, 1312)   0           conv4_block32_concat[0][0]       \n",
      "                                                                 conv4_block33_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_0_bn (BatchNormal (None, 4, 4, 1312)   5248        conv4_block33_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_0_relu (Activatio (None, 4, 4, 1312)   0           conv4_block34_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_1_conv (Conv2D)   (None, 4, 4, 128)    167936      conv4_block34_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block34_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block34_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block34_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_concat (Concatena (None, 4, 4, 1344)   0           conv4_block33_concat[0][0]       \n",
      "                                                                 conv4_block34_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_0_bn (BatchNormal (None, 4, 4, 1344)   5376        conv4_block34_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_0_relu (Activatio (None, 4, 4, 1344)   0           conv4_block35_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_1_conv (Conv2D)   (None, 4, 4, 128)    172032      conv4_block35_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block35_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block35_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block35_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_concat (Concatena (None, 4, 4, 1376)   0           conv4_block34_concat[0][0]       \n",
      "                                                                 conv4_block35_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_0_bn (BatchNormal (None, 4, 4, 1376)   5504        conv4_block35_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_0_relu (Activatio (None, 4, 4, 1376)   0           conv4_block36_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_1_conv (Conv2D)   (None, 4, 4, 128)    176128      conv4_block36_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block36_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block36_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block36_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_concat (Concatena (None, 4, 4, 1408)   0           conv4_block35_concat[0][0]       \n",
      "                                                                 conv4_block36_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_0_bn (BatchNormal (None, 4, 4, 1408)   5632        conv4_block36_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_0_relu (Activatio (None, 4, 4, 1408)   0           conv4_block37_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_1_conv (Conv2D)   (None, 4, 4, 128)    180224      conv4_block37_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block37_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block37_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block37_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block37_concat (Concatena (None, 4, 4, 1440)   0           conv4_block36_concat[0][0]       \n",
      "                                                                 conv4_block37_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_0_bn (BatchNormal (None, 4, 4, 1440)   5760        conv4_block37_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_0_relu (Activatio (None, 4, 4, 1440)   0           conv4_block38_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_1_conv (Conv2D)   (None, 4, 4, 128)    184320      conv4_block38_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block38_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block38_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block38_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block38_concat (Concatena (None, 4, 4, 1472)   0           conv4_block37_concat[0][0]       \n",
      "                                                                 conv4_block38_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_0_bn (BatchNormal (None, 4, 4, 1472)   5888        conv4_block38_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_0_relu (Activatio (None, 4, 4, 1472)   0           conv4_block39_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_1_conv (Conv2D)   (None, 4, 4, 128)    188416      conv4_block39_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block39_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block39_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block39_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block39_concat (Concatena (None, 4, 4, 1504)   0           conv4_block38_concat[0][0]       \n",
      "                                                                 conv4_block39_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_0_bn (BatchNormal (None, 4, 4, 1504)   6016        conv4_block39_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_0_relu (Activatio (None, 4, 4, 1504)   0           conv4_block40_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_1_conv (Conv2D)   (None, 4, 4, 128)    192512      conv4_block40_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block40_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block40_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block40_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block40_concat (Concatena (None, 4, 4, 1536)   0           conv4_block39_concat[0][0]       \n",
      "                                                                 conv4_block40_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_0_bn (BatchNormal (None, 4, 4, 1536)   6144        conv4_block40_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_0_relu (Activatio (None, 4, 4, 1536)   0           conv4_block41_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_1_conv (Conv2D)   (None, 4, 4, 128)    196608      conv4_block41_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block41_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block41_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block41_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block41_concat (Concatena (None, 4, 4, 1568)   0           conv4_block40_concat[0][0]       \n",
      "                                                                 conv4_block41_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_0_bn (BatchNormal (None, 4, 4, 1568)   6272        conv4_block41_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_0_relu (Activatio (None, 4, 4, 1568)   0           conv4_block42_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_1_conv (Conv2D)   (None, 4, 4, 128)    200704      conv4_block42_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block42_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block42_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block42_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block42_concat (Concatena (None, 4, 4, 1600)   0           conv4_block41_concat[0][0]       \n",
      "                                                                 conv4_block42_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_0_bn (BatchNormal (None, 4, 4, 1600)   6400        conv4_block42_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_0_relu (Activatio (None, 4, 4, 1600)   0           conv4_block43_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_1_conv (Conv2D)   (None, 4, 4, 128)    204800      conv4_block43_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block43_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block43_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block43_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block43_concat (Concatena (None, 4, 4, 1632)   0           conv4_block42_concat[0][0]       \n",
      "                                                                 conv4_block43_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_0_bn (BatchNormal (None, 4, 4, 1632)   6528        conv4_block43_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_0_relu (Activatio (None, 4, 4, 1632)   0           conv4_block44_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_1_conv (Conv2D)   (None, 4, 4, 128)    208896      conv4_block44_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block44_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block44_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block44_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block44_concat (Concatena (None, 4, 4, 1664)   0           conv4_block43_concat[0][0]       \n",
      "                                                                 conv4_block44_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_0_bn (BatchNormal (None, 4, 4, 1664)   6656        conv4_block44_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_0_relu (Activatio (None, 4, 4, 1664)   0           conv4_block45_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_1_conv (Conv2D)   (None, 4, 4, 128)    212992      conv4_block45_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block45_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block45_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block45_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block45_concat (Concatena (None, 4, 4, 1696)   0           conv4_block44_concat[0][0]       \n",
      "                                                                 conv4_block45_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_0_bn (BatchNormal (None, 4, 4, 1696)   6784        conv4_block45_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_0_relu (Activatio (None, 4, 4, 1696)   0           conv4_block46_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_1_conv (Conv2D)   (None, 4, 4, 128)    217088      conv4_block46_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block46_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block46_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block46_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block46_concat (Concatena (None, 4, 4, 1728)   0           conv4_block45_concat[0][0]       \n",
      "                                                                 conv4_block46_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_0_bn (BatchNormal (None, 4, 4, 1728)   6912        conv4_block46_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_0_relu (Activatio (None, 4, 4, 1728)   0           conv4_block47_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_1_conv (Conv2D)   (None, 4, 4, 128)    221184      conv4_block47_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block47_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block47_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block47_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block47_concat (Concatena (None, 4, 4, 1760)   0           conv4_block46_concat[0][0]       \n",
      "                                                                 conv4_block47_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_0_bn (BatchNormal (None, 4, 4, 1760)   7040        conv4_block47_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_0_relu (Activatio (None, 4, 4, 1760)   0           conv4_block48_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_1_conv (Conv2D)   (None, 4, 4, 128)    225280      conv4_block48_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_1_bn (BatchNormal (None, 4, 4, 128)    512         conv4_block48_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_1_relu (Activatio (None, 4, 4, 128)    0           conv4_block48_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv4_block48_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block48_concat (Concatena (None, 4, 4, 1792)   0           conv4_block47_concat[0][0]       \n",
      "                                                                 conv4_block48_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_bn (BatchNormalization)   (None, 4, 4, 1792)   7168        conv4_block48_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pool4_relu (Activation)         (None, 4, 4, 1792)   0           pool4_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool4_conv (Conv2D)             (None, 4, 4, 896)    1605632     pool4_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool4_pool (AveragePooling2D)   (None, 2, 2, 896)    0           pool4_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 2, 2, 896)    3584        pool4_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_relu (Activation (None, 2, 2, 896)    0           conv5_block1_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 2, 2, 128)    114688      conv5_block1_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 2, 2, 128)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_concat (Concatenat (None, 2, 2, 928)    0           pool4_pool[0][0]                 \n",
      "                                                                 conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_bn (BatchNormali (None, 2, 2, 928)    3712        conv5_block1_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_0_relu (Activation (None, 2, 2, 928)    0           conv5_block2_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 2, 2, 128)    118784      conv5_block2_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 2, 2, 128)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_concat (Concatenat (None, 2, 2, 960)    0           conv5_block1_concat[0][0]        \n",
      "                                                                 conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_bn (BatchNormali (None, 2, 2, 960)    3840        conv5_block2_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_0_relu (Activation (None, 2, 2, 960)    0           conv5_block3_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 2, 2, 128)    122880      conv5_block3_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 2, 2, 128)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_concat (Concatenat (None, 2, 2, 992)    0           conv5_block2_concat[0][0]        \n",
      "                                                                 conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_bn (BatchNormali (None, 2, 2, 992)    3968        conv5_block3_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_0_relu (Activation (None, 2, 2, 992)    0           conv5_block4_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_conv (Conv2D)    (None, 2, 2, 128)    126976      conv5_block4_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_1_relu (Activation (None, 2, 2, 128)    0           conv5_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block4_concat (Concatenat (None, 2, 2, 1024)   0           conv5_block3_concat[0][0]        \n",
      "                                                                 conv5_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_bn (BatchNormali (None, 2, 2, 1024)   4096        conv5_block4_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_0_relu (Activation (None, 2, 2, 1024)   0           conv5_block5_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_conv (Conv2D)    (None, 2, 2, 128)    131072      conv5_block5_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_1_relu (Activation (None, 2, 2, 128)    0           conv5_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block5_concat (Concatenat (None, 2, 2, 1056)   0           conv5_block4_concat[0][0]        \n",
      "                                                                 conv5_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_bn (BatchNormali (None, 2, 2, 1056)   4224        conv5_block5_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_0_relu (Activation (None, 2, 2, 1056)   0           conv5_block6_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_conv (Conv2D)    (None, 2, 2, 128)    135168      conv5_block6_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_1_relu (Activation (None, 2, 2, 128)    0           conv5_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block6_concat (Concatenat (None, 2, 2, 1088)   0           conv5_block5_concat[0][0]        \n",
      "                                                                 conv5_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_bn (BatchNormali (None, 2, 2, 1088)   4352        conv5_block6_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_0_relu (Activation (None, 2, 2, 1088)   0           conv5_block7_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_conv (Conv2D)    (None, 2, 2, 128)    139264      conv5_block7_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_1_relu (Activation (None, 2, 2, 128)    0           conv5_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block7_concat (Concatenat (None, 2, 2, 1120)   0           conv5_block6_concat[0][0]        \n",
      "                                                                 conv5_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_bn (BatchNormali (None, 2, 2, 1120)   4480        conv5_block7_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_0_relu (Activation (None, 2, 2, 1120)   0           conv5_block8_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_conv (Conv2D)    (None, 2, 2, 128)    143360      conv5_block8_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_1_relu (Activation (None, 2, 2, 128)    0           conv5_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block8_concat (Concatenat (None, 2, 2, 1152)   0           conv5_block7_concat[0][0]        \n",
      "                                                                 conv5_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_bn (BatchNormali (None, 2, 2, 1152)   4608        conv5_block8_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_0_relu (Activation (None, 2, 2, 1152)   0           conv5_block9_0_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_conv (Conv2D)    (None, 2, 2, 128)    147456      conv5_block9_0_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_bn (BatchNormali (None, 2, 2, 128)    512         conv5_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_1_relu (Activation (None, 2, 2, 128)    0           conv5_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv5_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block9_concat (Concatenat (None, 2, 2, 1184)   0           conv5_block8_concat[0][0]        \n",
      "                                                                 conv5_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_bn (BatchNormal (None, 2, 2, 1184)   4736        conv5_block9_concat[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_0_relu (Activatio (None, 2, 2, 1184)   0           conv5_block10_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_conv (Conv2D)   (None, 2, 2, 128)    151552      conv5_block10_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block10_concat (Concatena (None, 2, 2, 1216)   0           conv5_block9_concat[0][0]        \n",
      "                                                                 conv5_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_bn (BatchNormal (None, 2, 2, 1216)   4864        conv5_block10_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_0_relu (Activatio (None, 2, 2, 1216)   0           conv5_block11_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_conv (Conv2D)   (None, 2, 2, 128)    155648      conv5_block11_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block11_concat (Concatena (None, 2, 2, 1248)   0           conv5_block10_concat[0][0]       \n",
      "                                                                 conv5_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_bn (BatchNormal (None, 2, 2, 1248)   4992        conv5_block11_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_0_relu (Activatio (None, 2, 2, 1248)   0           conv5_block12_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_conv (Conv2D)   (None, 2, 2, 128)    159744      conv5_block12_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block12_concat (Concatena (None, 2, 2, 1280)   0           conv5_block11_concat[0][0]       \n",
      "                                                                 conv5_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_bn (BatchNormal (None, 2, 2, 1280)   5120        conv5_block12_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_0_relu (Activatio (None, 2, 2, 1280)   0           conv5_block13_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_conv (Conv2D)   (None, 2, 2, 128)    163840      conv5_block13_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block13_concat (Concatena (None, 2, 2, 1312)   0           conv5_block12_concat[0][0]       \n",
      "                                                                 conv5_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_bn (BatchNormal (None, 2, 2, 1312)   5248        conv5_block13_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_0_relu (Activatio (None, 2, 2, 1312)   0           conv5_block14_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_conv (Conv2D)   (None, 2, 2, 128)    167936      conv5_block14_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block14_concat (Concatena (None, 2, 2, 1344)   0           conv5_block13_concat[0][0]       \n",
      "                                                                 conv5_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_bn (BatchNormal (None, 2, 2, 1344)   5376        conv5_block14_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_0_relu (Activatio (None, 2, 2, 1344)   0           conv5_block15_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_conv (Conv2D)   (None, 2, 2, 128)    172032      conv5_block15_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block15_concat (Concatena (None, 2, 2, 1376)   0           conv5_block14_concat[0][0]       \n",
      "                                                                 conv5_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_bn (BatchNormal (None, 2, 2, 1376)   5504        conv5_block15_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_0_relu (Activatio (None, 2, 2, 1376)   0           conv5_block16_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_conv (Conv2D)   (None, 2, 2, 128)    176128      conv5_block16_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block16_concat (Concatena (None, 2, 2, 1408)   0           conv5_block15_concat[0][0]       \n",
      "                                                                 conv5_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_0_bn (BatchNormal (None, 2, 2, 1408)   5632        conv5_block16_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_0_relu (Activatio (None, 2, 2, 1408)   0           conv5_block17_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_1_conv (Conv2D)   (None, 2, 2, 128)    180224      conv5_block17_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block17_concat (Concatena (None, 2, 2, 1440)   0           conv5_block16_concat[0][0]       \n",
      "                                                                 conv5_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_0_bn (BatchNormal (None, 2, 2, 1440)   5760        conv5_block17_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_0_relu (Activatio (None, 2, 2, 1440)   0           conv5_block18_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_1_conv (Conv2D)   (None, 2, 2, 128)    184320      conv5_block18_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block18_concat (Concatena (None, 2, 2, 1472)   0           conv5_block17_concat[0][0]       \n",
      "                                                                 conv5_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_0_bn (BatchNormal (None, 2, 2, 1472)   5888        conv5_block18_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_0_relu (Activatio (None, 2, 2, 1472)   0           conv5_block19_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_1_conv (Conv2D)   (None, 2, 2, 128)    188416      conv5_block19_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block19_concat (Concatena (None, 2, 2, 1504)   0           conv5_block18_concat[0][0]       \n",
      "                                                                 conv5_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_0_bn (BatchNormal (None, 2, 2, 1504)   6016        conv5_block19_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_0_relu (Activatio (None, 2, 2, 1504)   0           conv5_block20_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_1_conv (Conv2D)   (None, 2, 2, 128)    192512      conv5_block20_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block20_concat (Concatena (None, 2, 2, 1536)   0           conv5_block19_concat[0][0]       \n",
      "                                                                 conv5_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_0_bn (BatchNormal (None, 2, 2, 1536)   6144        conv5_block20_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_0_relu (Activatio (None, 2, 2, 1536)   0           conv5_block21_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_1_conv (Conv2D)   (None, 2, 2, 128)    196608      conv5_block21_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block21_concat (Concatena (None, 2, 2, 1568)   0           conv5_block20_concat[0][0]       \n",
      "                                                                 conv5_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_0_bn (BatchNormal (None, 2, 2, 1568)   6272        conv5_block21_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_0_relu (Activatio (None, 2, 2, 1568)   0           conv5_block22_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_1_conv (Conv2D)   (None, 2, 2, 128)    200704      conv5_block22_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block22_concat (Concatena (None, 2, 2, 1600)   0           conv5_block21_concat[0][0]       \n",
      "                                                                 conv5_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_0_bn (BatchNormal (None, 2, 2, 1600)   6400        conv5_block22_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_0_relu (Activatio (None, 2, 2, 1600)   0           conv5_block23_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_1_conv (Conv2D)   (None, 2, 2, 128)    204800      conv5_block23_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block23_concat (Concatena (None, 2, 2, 1632)   0           conv5_block22_concat[0][0]       \n",
      "                                                                 conv5_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_0_bn (BatchNormal (None, 2, 2, 1632)   6528        conv5_block23_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_0_relu (Activatio (None, 2, 2, 1632)   0           conv5_block24_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_1_conv (Conv2D)   (None, 2, 2, 128)    208896      conv5_block24_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block24_concat (Concatena (None, 2, 2, 1664)   0           conv5_block23_concat[0][0]       \n",
      "                                                                 conv5_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_0_bn (BatchNormal (None, 2, 2, 1664)   6656        conv5_block24_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_0_relu (Activatio (None, 2, 2, 1664)   0           conv5_block25_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_1_conv (Conv2D)   (None, 2, 2, 128)    212992      conv5_block25_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block25_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block25_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block25_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block25_concat (Concatena (None, 2, 2, 1696)   0           conv5_block24_concat[0][0]       \n",
      "                                                                 conv5_block25_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_0_bn (BatchNormal (None, 2, 2, 1696)   6784        conv5_block25_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_0_relu (Activatio (None, 2, 2, 1696)   0           conv5_block26_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_1_conv (Conv2D)   (None, 2, 2, 128)    217088      conv5_block26_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block26_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block26_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block26_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block26_concat (Concatena (None, 2, 2, 1728)   0           conv5_block25_concat[0][0]       \n",
      "                                                                 conv5_block26_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_0_bn (BatchNormal (None, 2, 2, 1728)   6912        conv5_block26_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_0_relu (Activatio (None, 2, 2, 1728)   0           conv5_block27_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_1_conv (Conv2D)   (None, 2, 2, 128)    221184      conv5_block27_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block27_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block27_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block27_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block27_concat (Concatena (None, 2, 2, 1760)   0           conv5_block26_concat[0][0]       \n",
      "                                                                 conv5_block27_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_0_bn (BatchNormal (None, 2, 2, 1760)   7040        conv5_block27_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_0_relu (Activatio (None, 2, 2, 1760)   0           conv5_block28_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_1_conv (Conv2D)   (None, 2, 2, 128)    225280      conv5_block28_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block28_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block28_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block28_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block28_concat (Concatena (None, 2, 2, 1792)   0           conv5_block27_concat[0][0]       \n",
      "                                                                 conv5_block28_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_0_bn (BatchNormal (None, 2, 2, 1792)   7168        conv5_block28_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_0_relu (Activatio (None, 2, 2, 1792)   0           conv5_block29_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_1_conv (Conv2D)   (None, 2, 2, 128)    229376      conv5_block29_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block29_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block29_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block29_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block29_concat (Concatena (None, 2, 2, 1824)   0           conv5_block28_concat[0][0]       \n",
      "                                                                 conv5_block29_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_0_bn (BatchNormal (None, 2, 2, 1824)   7296        conv5_block29_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_0_relu (Activatio (None, 2, 2, 1824)   0           conv5_block30_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_1_conv (Conv2D)   (None, 2, 2, 128)    233472      conv5_block30_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block30_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block30_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block30_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block30_concat (Concatena (None, 2, 2, 1856)   0           conv5_block29_concat[0][0]       \n",
      "                                                                 conv5_block30_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_0_bn (BatchNormal (None, 2, 2, 1856)   7424        conv5_block30_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_0_relu (Activatio (None, 2, 2, 1856)   0           conv5_block31_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_1_conv (Conv2D)   (None, 2, 2, 128)    237568      conv5_block31_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block31_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block31_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block31_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block31_concat (Concatena (None, 2, 2, 1888)   0           conv5_block30_concat[0][0]       \n",
      "                                                                 conv5_block31_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_0_bn (BatchNormal (None, 2, 2, 1888)   7552        conv5_block31_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_0_relu (Activatio (None, 2, 2, 1888)   0           conv5_block32_0_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_1_conv (Conv2D)   (None, 2, 2, 128)    241664      conv5_block32_0_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_1_bn (BatchNormal (None, 2, 2, 128)    512         conv5_block32_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_1_relu (Activatio (None, 2, 2, 128)    0           conv5_block32_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv5_block32_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block32_concat (Concatena (None, 2, 2, 1920)   0           conv5_block31_concat[0][0]       \n",
      "                                                                 conv5_block32_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "bn (BatchNormalization)         (None, 2, 2, 1920)   7680        conv5_block32_concat[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "relu (Activation)               (None, 2, 2, 1920)   0           bn[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "max_pool (GlobalMaxPooling2D)   (None, 1920)         0           relu[0][0]                       \n",
      "==================================================================================================\n",
      "Total params: 18,321,984\n",
      "Trainable params: 18,092,928\n",
      "Non-trainable params: 229,056\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "densenet = DenseNet201(input_shape=IMG_SHAPE, \n",
    "                       include_top=False, \n",
    "                       weights='imagenet',\n",
    "                       pooling='max',\n",
    "                       classes=1000)\n",
    "\n",
    "densenet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:20.349795Z",
     "start_time": "2020-06-21T21:51:19.407452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([32, 1920])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_batch = densenet(image_batch)\n",
    "feature_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:20.356092Z",
     "start_time": "2020-06-21T21:51:20.351156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "708"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(densenet.layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add new layers to model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:25.465486Z",
     "start_time": "2020-06-21T21:51:20.358579Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    densenet,\n",
    "    BatchNormalization(),\n",
    "    Dense(2048, activation='relu', kernel_regularizer=l1_l2(0.01)),\n",
    "    BatchNormalization(),\n",
    "    Dense(2048, activation='relu', kernel_regularizer=l1_l2(0.01)),\n",
    "    BatchNormalization(),\n",
    "    Dense(2048, activation='relu', kernel_regularizer=l1_l2(0.01)),\n",
    "    BatchNormalization(),\n",
    "    Dense(2048, activation='relu', kernel_regularizer=l1_l2(0.01)),\n",
    "    BatchNormalization(),\n",
    "    Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:25.482820Z",
     "start_time": "2020-06-21T21:51:25.466822Z"
    }
   },
   "outputs": [],
   "source": [
    "for layer in model.layers:\n",
    "    layer.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:25.525977Z",
     "start_time": "2020-06-21T21:51:25.484361Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer=Adam(lr=1e-4),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:25.573066Z",
     "start_time": "2020-06-21T21:51:25.527400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "densenet201 (Model)          (None, 1920)              18321984  \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 1920)              7680      \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2048)              3934208   \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2048)              4196352   \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2048)              4196352   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2048)              4196352   \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                20490     \n",
      "=================================================================\n",
      "Total params: 34,906,186\n",
      "Trainable params: 34,656,906\n",
      "Non-trainable params: 249,280\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute baseline metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:31.080542Z",
     "start_time": "2020-06-21T21:51:25.574150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 0s 24ms/step - loss: 3255.1558 - accuracy: 0.1063\n"
     ]
    }
   ],
   "source": [
    "initial_epochs = 10\n",
    "validation_steps = 20\n",
    "\n",
    "initial_loss, initial_accuracy = model.evaluate(validation_batches, steps = validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:31.084600Z",
     "start_time": "2020-06-21T21:51:31.081825Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss: 3255.16 | initial_accuracy accuracy: 10.63%\n"
     ]
    }
   ],
   "source": [
    "print(f'Initial loss: {initial_loss:.2f} | initial_accuracy accuracy: {initial_accuracy:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:31.098158Z",
     "start_time": "2020-06-21T21:51:31.086161Z"
    }
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_accuracy', \n",
    "                               patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:31.106895Z",
     "start_time": "2020-06-21T21:51:31.099161Z"
    }
   },
   "outputs": [],
   "source": [
    "eurosat_path = (results_path / 'cnn.weights.best.hdf5').as_posix()\n",
    "checkpointer = ModelCheckpoint(filepath=eurosat_path,\n",
    "                               verbose=1,\n",
    "                               monitor='val_accuracy',\n",
    "                               save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T21:51:31.116320Z",
     "start_time": "2020-06-21T21:51:31.107974Z"
    }
   },
   "outputs": [],
   "source": [
    "epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:34:30.076912Z",
     "start_time": "2020-06-21T21:51:31.117479Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "    760/Unknown - 70s 92ms/step - loss: 700.4531 - accuracy: 0.8181\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.94444, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 74s 97ms/step - loss: 700.4531 - accuracy: 0.8181 - val_loss: 4.1437 - val_accuracy: 0.9444\n",
      "Epoch 2/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.5708 - accuracy: 0.9230\n",
      "Epoch 00002: val_accuracy improved from 0.94444 to 0.94963, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 75s 98ms/step - loss: 2.5708 - accuracy: 0.9230 - val_loss: 2.3430 - val_accuracy: 0.9496\n",
      "Epoch 3/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.3192 - accuracy: 0.9536\n",
      "Epoch 00003: val_accuracy did not improve from 0.94963\n",
      "760/760 [==============================] - 71s 94ms/step - loss: 2.3192 - accuracy: 0.9536 - val_loss: 2.5888 - val_accuracy: 0.9107\n",
      "Epoch 4/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.3036 - accuracy: 0.9686\n",
      "Epoch 00004: val_accuracy improved from 0.94963 to 0.96407, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 83s 109ms/step - loss: 2.3036 - accuracy: 0.9686 - val_loss: 2.5276 - val_accuracy: 0.9641\n",
      "Epoch 5/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.1611 - accuracy: 0.9746\n",
      "Epoch 00005: val_accuracy did not improve from 0.96407\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 2.1611 - accuracy: 0.9746 - val_loss: 2.2576 - val_accuracy: 0.9496\n",
      "Epoch 6/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.1564 - accuracy: 0.9795\n",
      "Epoch 00006: val_accuracy did not improve from 0.96407\n",
      "760/760 [==============================] - 101s 133ms/step - loss: 2.1564 - accuracy: 0.9795 - val_loss: 2.3695 - val_accuracy: 0.9633\n",
      "Epoch 7/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0421 - accuracy: 0.9819\n",
      "Epoch 00007: val_accuracy did not improve from 0.96407\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 2.0421 - accuracy: 0.9819 - val_loss: 2.0737 - val_accuracy: 0.9641\n",
      "Epoch 8/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0481 - accuracy: 0.9845\n",
      "Epoch 00008: val_accuracy did not improve from 0.96407\n",
      "760/760 [==============================] - 102s 135ms/step - loss: 2.0481 - accuracy: 0.9845 - val_loss: 2.3596 - val_accuracy: 0.9385\n",
      "Epoch 9/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.2188 - accuracy: 0.9676\n",
      "Epoch 00009: val_accuracy did not improve from 0.96407\n",
      "760/760 [==============================] - 101s 133ms/step - loss: 2.2188 - accuracy: 0.9676 - val_loss: 2.5635 - val_accuracy: 0.8733\n",
      "Epoch 10/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0949 - accuracy: 0.9826\n",
      "Epoch 00010: val_accuracy did not improve from 0.96407\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 2.0949 - accuracy: 0.9826 - val_loss: 2.3571 - val_accuracy: 0.9470\n",
      "Epoch 11/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0105 - accuracy: 0.9886\n",
      "Epoch 00011: val_accuracy improved from 0.96407 to 0.96704, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 105s 138ms/step - loss: 2.0105 - accuracy: 0.9886 - val_loss: 2.0671 - val_accuracy: 0.9670\n",
      "Epoch 12/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 1.9271 - accuracy: 0.9895\n",
      "Epoch 00012: val_accuracy improved from 0.96704 to 0.97630, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 106s 140ms/step - loss: 1.9271 - accuracy: 0.9895 - val_loss: 1.9610 - val_accuracy: 0.9763\n",
      "Epoch 13/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0319 - accuracy: 0.9854\n",
      "Epoch 00013: val_accuracy did not improve from 0.97630\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 2.0319 - accuracy: 0.9854 - val_loss: 2.4892 - val_accuracy: 0.9419\n",
      "Epoch 14/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0903 - accuracy: 0.9786\n",
      "Epoch 00014: val_accuracy improved from 0.97630 to 0.97704, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 2.0903 - accuracy: 0.9786 - val_loss: 2.0443 - val_accuracy: 0.9770\n",
      "Epoch 15/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 1.9076 - accuracy: 0.9940\n",
      "Epoch 00015: val_accuracy improved from 0.97704 to 0.98074, saving model to results/eurosat/cnn.weights.best.hdf5\n",
      "760/760 [==============================] - 104s 137ms/step - loss: 1.9076 - accuracy: 0.9940 - val_loss: 1.9274 - val_accuracy: 0.9807\n",
      "Epoch 16/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0973 - accuracy: 0.9888\n",
      "Epoch 00016: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 103s 135ms/step - loss: 2.0973 - accuracy: 0.9888 - val_loss: 2.2508 - val_accuracy: 0.9648\n",
      "Epoch 17/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0802 - accuracy: 0.9858\n",
      "Epoch 00017: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 102s 135ms/step - loss: 2.0802 - accuracy: 0.9858 - val_loss: 2.0436 - val_accuracy: 0.9619\n",
      "Epoch 18/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0126 - accuracy: 0.9894\n",
      "Epoch 00018: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 110s 145ms/step - loss: 2.0126 - accuracy: 0.9894 - val_loss: 2.0002 - val_accuracy: 0.9752\n",
      "Epoch 19/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0331 - accuracy: 0.9859\n",
      "Epoch 00019: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 130s 170ms/step - loss: 2.0331 - accuracy: 0.9859 - val_loss: 1.9779 - val_accuracy: 0.9726\n",
      "Epoch 20/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 1.9184 - accuracy: 0.9937\n",
      "Epoch 00020: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 131s 172ms/step - loss: 1.9184 - accuracy: 0.9937 - val_loss: 1.9657 - val_accuracy: 0.9748\n",
      "Epoch 21/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 1.8805 - accuracy: 0.9951\n",
      "Epoch 00021: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 132s 173ms/step - loss: 1.8805 - accuracy: 0.9951 - val_loss: 2.2087 - val_accuracy: 0.9204\n",
      "Epoch 22/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.3553 - accuracy: 0.9720\n",
      "Epoch 00022: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 105s 139ms/step - loss: 2.3553 - accuracy: 0.9720 - val_loss: 4.7075 - val_accuracy: 0.8170\n",
      "Epoch 23/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 2.0543 - accuracy: 0.9889\n",
      "Epoch 00023: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 2.0543 - accuracy: 0.9889 - val_loss: 2.2079 - val_accuracy: 0.9737\n",
      "Epoch 24/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 1.9796 - accuracy: 0.9888\n",
      "Epoch 00024: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 103s 135ms/step - loss: 1.9796 - accuracy: 0.9888 - val_loss: 7.1897 - val_accuracy: 0.7819\n",
      "Epoch 25/100\n",
      "760/760 [==============================] - ETA: 0s - loss: 1.9561 - accuracy: 0.9883\n",
      "Epoch 00025: val_accuracy did not improve from 0.98074\n",
      "760/760 [==============================] - 102s 134ms/step - loss: 1.9561 - accuracy: 0.9883 - val_loss: 1.9583 - val_accuracy: 0.9763\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_batches,\n",
    "                    epochs=epochs,\n",
    "                    validation_data=validation_batches,\n",
    "                    callbacks=[checkpointer, \n",
    "                               early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Learning Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:34:30.087982Z",
     "start_time": "2020-06-21T22:34:30.080260Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_learning_curves(df):\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(15, 4))\n",
    "    df[['accuracy', 'val_accuracy']].plot(ax=axes[0], title='Accuracy')\n",
    "    df[['loss', 'val_loss']].plot(ax=axes[1], title='Cross-Entropy')\n",
    "    for ax in axes:\n",
    "        ax.legend(['Training', 'Validation'])\n",
    "    fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T22:34:31.387392Z",
     "start_time": "2020-06-21T22:34:30.090413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = pd.DataFrame(history.history)\n",
    "metrics.index = metrics.index.to_series().add(1)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(15, 4))\n",
    "metrics[['accuracy', 'val_accuracy']].plot(ax=axes[0], \n",
    "                                           title=f'Accuracy (Best: {metrics.val_accuracy.max():.2%})')\n",
    "axes[0].axvline(metrics.val_accuracy.idxmax(), ls='--', lw=1, c='k')\n",
    "metrics[['loss', 'val_loss']].plot(ax=axes[1], title='Cross-Entropy', logy=True)\n",
    "for ax in axes:\n",
    "    ax.legend(['Training', 'Validation'])\n",
    "    ax.set_xlabel('Epoch')\n",
    "    \n",
    "axes[0].yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y)))\n",
    "axes[0].set_ylabel('Accuracy')\n",
    "axes[1].set_ylabel('Loss')\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'satellite_accuracy', dpi=300);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t-dl]",
   "language": "python",
   "name": "conda-env-ml4t-dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "318.55px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}